In the wind energy industry, it is of great importance to develop models that accurately forecast the power output of a wind turbine, as such predictions are used for wind farm location assessment or power pricing and bidding, monitoring, and preventive maintenance. As a first step, and following the guidelines of the existing literature, we use the supervisory control and data acquisition (SCADA) data to model the wind turbine power curve (WTPC). We explore various parametric and non-parametric approaches for the modeling of the WTPC, such as parametric logistic functions, and non-parametric piecewise linear, polynomial, or cubic spline interpolation functions. We demonstrate that all aforementioned classes of models are rich enough (with respect to their relative complexity) to accurately model the WTPC, as their mean squared error (MSE) is close to the MSE lower bound calculated from the historical data. We further enhance the accuracy of our proposed model, by incorporating additional environmental factors that affect the power output, such as the ambient temperature, and the wind direction. However, all aforementioned models, when it comes to forecasting, seem to have an intrinsic limitation, due to their inability to capture the inherent auto-correlation of the data. To avoid this conundrum, we show that adding a properly scaled ARMA modeling layer increases short-term prediction performance, while keeping the long-term prediction capability of the model.
翻译:在风能产业中,非常重要的是开发准确预测风力涡轮机动力输出的模型,因为此类预测用于风力农场地点评估或电价和招标、监测和预防性维护。作为第一步,根据现有文献的指导方针,我们使用监督控制和数据获取数据来模拟风力涡轮动力曲线(WTPC),我们探索了各种参数和非参数方法,以模拟风力涡轮机动力输出,如准逻辑后勤功能,以及非对称的线性、多元螺旋或立方螺旋内插功能。我们证明,上述所有类型的模型都足够丰富(相对复杂性),可以准确模拟WTPC,因为它们的平均正方差(MSE)接近从历史数据中计算的最低约束的MSE。我们进一步增加了我们拟议模型的准确性,纳入了影响模型输出的其他环境因素,如环境温度和风向。然而,所有上述模型在预测时似乎都具有内在的局限性(就其相对复杂性而言),因为其平均性差差(MSE)接近WPC,因为其平均正方差差差差(MSE)接近从历史数据层中得出了我们无法正确测算的内在数据。